HRLSim: A High Performance Spiking Neural Network Simulator for GPGPU Clusters
HRLSim: A High Performance Spiking Neural Network Simulator for GPGPU Clusters
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DOI：
10.1109/tnnls.2013.2276056
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发表时间：
2014-02-01
影响因子：
10.4
通讯作者：
Srinivasa, Narayan
中科院分区：
文献类型：
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作者：
Minkovich, Kirill;Thibeault, Corey M.;Srinivasa, Narayan
大规模脉冲神经模型的建模是理解大脑功能并随后创建现实世界应用的重要工具。本文介绍了一个脉冲神经网络模拟器环境，称为HRL脉冲模拟器（HRLSim）。该模拟器适合于在通用图形处理单元（GPGPU）集群上实现。HRLSim的新方面进行了描述，并提供了各种配置的集群的性能分析。随着廉价的GPGPU卡和计算能力的出现，HRLSim为设计、实时仿真和分析大规模尖峰神经网络提供了一个经济实惠且可扩展的工具。
Modeling of large-scale spiking neural models is an important tool in the quest to understand brain function and subsequently create real-world applications. This paper describes a spiking neural network simulator environment called HRL Spiking Simulator (HRLSim). This simulator is suitable for implementation on a cluster of general purpose graphical processing units (GPGPUs). Novel aspects of HRLSim are described and an analysis of its performance is provided for various configurations of the cluster. With the advent of inexpensive GPGPU cards and compute power, HRLSim offers an affordable and scalable tool for design, real-time simulation, and analysis of large-scale spiking neural networks.